Lifetime psychopathology among the offspring of Bipolar I parents

BACKGROUND: Recent studies have demonstrated high rates of psychopathology in the offspring of parents with bipolar disorder. The aim of this study was to identify psychiatric diagnoses in a sample of children of bipolar parents. METHOD: This case series comprised 35 children and adolescents aged 6 to 17 years, with a mean age of 12.5 + 2.9 years (20 males and 15 females), who had at least one parent with bipolar disorder type I. The subjects were assessed using the Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime version (K-SADS-PL). Family psychiatric history and demographics were also evaluated. RESULTS: Of the offspring studied, 71.4% had a lifetime diagnosis of at least one psychiatric disorder (28.6% with a mood disorder, 40% with a disruptive behavior disorder and 20% with an anxiety disorder). Pure mood disorders (11.4%) occurred less frequently than mood disorders comorbid with attention deficit hyperactivity disorder (17.1%). Psychopathology was commonly reported in second-degree relatives of the offspring of parents with bipolar disorder (71.4%). CONCLUSIONS: Our results support previous findings of an increased risk for developing psychopathology, predominantly mood and disruptive disorders, in the offspring of bipolar individuals. Prospective studies with larger samples are needed to confirm and expand these results.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)NARSADKrus Endowed Chair in Psychiatry UTHSCSAFederal University of São Paulo Department of PsychiatryThe University of Texas Health Science Center Departments of Psychiatry and OrthodonticsUniversidade de São Paulo Faculdade de Medicina Department of PsychiatryUniversity of Texas Health Science CenterThe University of Texas Health Science Center Department of PsychiatryUNIFESP, Department of PsychiatryNARSAD: MH 69774NARSAD: RR 20571NARSAD: MH068280SciEL

Lifetime psychopathology among the offspring of Bipolar I parents

BACKGROUND: Recent studies have demonstrated high rates of psychopathology in the offspring of parents with bipolar disorder. The aim of this study was to identify psychiatric diagnoses in a sample of children of bipolar parents. METHOD: This case series comprised 35 children and adolescents aged 6 to 17 years, with a mean age of 12.5 + 2.9 years (20 males and 15 females), who had at least one parent with bipolar disorder type I. The subjects were assessed using the Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime version (K-SADS-PL). Family psychiatric history and demographics were also evaluated. RESULTS: Of the offspring studied, 71.4% had a lifetime diagnosis of at least one psychiatric disorder (28.6% with a mood disorder, 40% with a disruptive behavior disorder and 20% with an anxiety disorder). Pure mood disorders (11.4%) occurred less frequently than mood disorders comorbid with attention deficit hyperactivity disorder (17.1%). Psychopathology was commonly reported in second-degree relatives of the offspring of parents with bipolar disorder (71.4%). CONCLUSIONS: Our results support previous findings of an increased risk for developing psychopathology, predominantly mood and disruptive disorders, in the offspring of bipolar individuals. Prospective studies with larger samples are needed to confirm and expand these results

Affective Processing in Pediatric Bipolar Disorder and Offspring of Bipolar Parents

Background: Bipolar disorder (BD) is characterized by biased processing of emotional information. However, little research in this area has been conducted in youth with BD and at-risk individuals. The goal of this study was to determine whether children with BD displayed comparable or more severe manifestations of this bias relative to offspring of parents with BD

Morphology of the subgenual prefrontal cortex in pediatric bipolar disorder

The subgenual prefrontal cortex (SGPFC) is an important brain region involved in emotional regulation and reward mechanisms. Volumetric abnormalities in this region have been identified in adults with bipolar disorder but thus far not in pediatric cases. We examined the volume of this brain region in subjects with pediatric bipolar disorder (PBD) and compared them to healthy controls

Candidate gene associations with mood disorder, cognitive vulnerability, and fronto-limbic volumes

BackgroundFour of the most consistently replicated variants associated with mood disorder occur in genes important for synaptic function: ANK3 (rs10994336), BDNF (rs6265), CACNA1C (rs1006737), and DGKH (rs1170191).AimsThe present study examined associations between these candidates, mood disorder diagnoses, cognition, and fronto-limbic regions implicated in affect regulation.Methods and materialsParticipants included 128 individuals with bipolar disorder (33% male, Mean age = 38.5), 48 with major depressive disorder (29% male, Mean age = 40.4), and 149 healthy controls (35% male, Mean age = 36.5). Genotypes were determined by 5′-fluorogenic exonuclease assays (TaqMan®). Fronto-limbic volumes were obtained from high resolution brain images using Freesurfer. Chi-square analyses, bivariate correlations, and mediational models examined relationships between genetic variants, mood diagnoses, cognitive measures, and brain volumes.ResultsCarriers of the minor BDNF and ANK3 alleles showed nonsignificant trends toward protective association in controls relative to mood disorder patients (P = 0.047). CACNA1C minor allele carriers had larger bilateral caudate, insula, globus pallidus, frontal pole, and nucleus accumbens volumes (smallest r = 0.13, P = 0.043), and increased IQ (r = 0.18, P < 0.001). CACNA1C associations with brain volumes and IQ were independent; larger fronto-limbic volumes did not mediate increased IQ. Other candidate variants were not significantly associated with diagnoses, cognition, or fronto-limbic volumes.Discussion and conclusionsCACNA1C may be associated with biological systems altered in mood disorder. Increases in fronto-limbic volumes and cognitive ability associated with CACNA1C minor allele genotypes are congruent with findings in healthy samples and may be a marker for increased risk for neuropsychiatric phenotypes. Even larger multimodal studies are needed to quantify the magnitude and specificity of genetic-imaging-cognition-symptom relationships

Brain‐age prediction: systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain‐age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain‐age has highlighted the need for robust and publicly available brain‐age models pre‐trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain‐age model. Here we expand this work to develop, empirically validate, and disseminate a pre‐trained brain‐age model to cover most of the human lifespan. To achieve this, we selected the best‐performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain‐age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre‐trained models were tested for cross‐dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age‐bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain‐age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open‐science, web‐based platform for individualized neuroimaging metrics

Brain‐age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.<br/

Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology

Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the enigma-anxiety working group

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology